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stability-ai/stable-diffusion

The original groundbreaking latent diffusion text-to-image model from Stability AI that helped democratize AI image generation. Stable Diffusion operates by learning a compression of images into a latent space and then learning to generate new images by gradually denoising random latent representations conditioned on text prompts. As the foundation model of a broad ecosystem, it supports diverse applications including text-to-image generation, image-to-image transformation, inpainting, and outpainting. While surpassed by newer models in some aspects, it remains a versatile and historically significant model that balances quality and computational efficiency.

Technical Specifications:

  • Architecture: Latent diffusion model
  • Resolution: 512x512 native with upscaling capabilities
  • Parameter Count: Approximately 1.5 billion parameters
  • Text Encoder: CLIP ViT-L/14
  • Inference Steps: Typically 20-50 for optimal quality
  • Resource Requirements: Can run on consumer GPUs with 8+ GB VRAM
  • Applications: Creative content generation, concept visualization, image editing


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